





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase 					=      random.Random()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Union[str, Any]=1.0       , UpperCamelCase__:				str=None       , UpperCamelCase__:				int=None     ):
		if rng is None:
				SCREAMING_SNAKE_CASE__								=   global_rng

		SCREAMING_SNAKE_CASE__								=   []
		for batch_idx in range(shape[0]     ):
				values.append([]     )
				for _ in range(shape[1]     ):
						values[-1].append(rng.random() * scale     )

		return values




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def __init__(				self     :Optional[Any]				,					__A     :str				,					__A     :int=7				,					__A     :Optional[Any]=400				,					__A     :Tuple=2000				,					__A     :Any=24				,					__A     :Optional[Any]=24				,					__A     :List[str]=0.0				,					__A     :Optional[int]=1_6000				,					__A     :Dict=True				,					__A     :List[Any]=True				,					) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   min_seq_length
									SCREAMING_SNAKE_CASE__								=   max_seq_length
									SCREAMING_SNAKE_CASE__								=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE__								=   feature_size
									SCREAMING_SNAKE_CASE__								=   num_mel_bins
									SCREAMING_SNAKE_CASE__								=   padding_value
									SCREAMING_SNAKE_CASE__								=   sampling_rate
									SCREAMING_SNAKE_CASE__								=   return_attention_mask
									SCREAMING_SNAKE_CASE__								=   do_normalize


							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									return {
									    "feature_size": self.feature_size,
									    "num_mel_bins": self.num_mel_bins,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									    "do_normalize": self.do_normalize,
									}


							def   _snake_case	(				self     :List[str]				,					__A     :str=False				,					__A     :Any=False		) -> Any:



									"""simple docstring"""



									def _flatten(__A     :Tuple		):
											return list(itertools.chain(*__A		)		)

									if equal_length:
											SCREAMING_SNAKE_CASE__								=   [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
									else:
											# make sure that inputs increase in size
											SCREAMING_SNAKE_CASE__								=   [
											    floats_list((x, self.feature_size)		)
											    for x in range(self.min_seq_length				,					self.max_seq_length				,					self.seq_length_diff		)
											]
									if numpify:
											SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       SpeechaTextFeatureExtractor if is_speech_available() else None


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   SpeechaTextFeatureExtractionTester(self		)


							def   _snake_case	(				self     :Dict				,					__A     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									self.assertTrue(np.all(np.mean(__A				,					axis=0		) < 1E-3		)		)
									self.assertTrue(np.all(np.abs(np.var(__A				,					axis=0		) - 1		) < 1E-3		)		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for speech_input in speech_inputs]

									# Test feature size
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					padding=__A				,					return_tensors="""np"""		).input_features
									self.assertTrue(input_features.ndim == 3		)
									self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__								=   feature_extractor(speech_inputs[0]				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(np_speech_inputs[0]				,					return_tensors="""np"""		).input_features
									self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test batched
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test 2-D numpy arrays are batched.
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
									SCREAMING_SNAKE_CASE__								=   np.asarray(__A		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					padding=__A				,					max_length=__A				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					max_length=__A				,					padding=__A				,					return_tensors="""np"""				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""max_length"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1]		)
									self._check_zero_mean_unit_variance(input_features[2]		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 4, 24)		)

									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=16				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 6, 24)		)


							def   _snake_case	(				self     :Any		) -> Union[str, Any]:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   np.random.rand(100				,					32		).astype(np.floataa		)
									SCREAMING_SNAKE_CASE__								=   np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
											SCREAMING_SNAKE_CASE__								=   feature_extractor.pad([{"""input_features""": inputs}]				,					return_tensors="""np"""		)
											self.assertTrue(np_processed.input_features.dtype == np.floataa		)
											SCREAMING_SNAKE_CASE__								=   feature_extractor.pad([{"""input_features""": inputs}]				,					return_tensors="""pt"""		)
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									from datasets import load_dataset

									SCREAMING_SNAKE_CASE__								=   load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,					"""clean"""				,					split="""validation"""		)
									# automatic decoding with librispeech
									SCREAMING_SNAKE_CASE__								=   ds.sort("""id"""		).select(range(__A		)		)[:num_samples]["""audio"""]

									return [x["array"] for x in speech_samples]


							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   np.array([
									    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
									    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
									    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
									]		)
									# fmt: on

									SCREAMING_SNAKE_CASE__								=   self._load_datasamples(1		)
									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""pt"""		).input_features
									self.assertEquals(input_features.shape				,					(1, 584, 24)		)
									self.assertTrue(np.allclose(input_features[0, 0, :30]				,					__A				,					atol=1E-4		)		)







'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"LayoutLMv2ImageProcessor"
					a_        =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




					def __init__(  self  :		Optional[int]				, _A  :		str=None				, _A  :		Optional[Any]=None				, **_A  :		Any      )    ->							Tuple:
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				, _A				, )
											UpperCAmelCase_      :       int          =       kwargs.pop('''feature_extractor'''      )

								UpperCAmelCase_      :       str          =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''      )
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''      )

								super().__init__(_A				, _A      )




					def __call__(  self  :		str				, _A  :		Optional[int]				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				, _A  :		Union[List[List[int]], List[List[List[int]]]] = None				, _A  :		Optional[Union[List[int], List[List[int]]]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes '''
											    '''if you initialized the image processor with apply_ocr set to True.'''      )

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

								# first, apply the image processor
								UpperCAmelCase_      :       int          =       self.image_processor(images=_A				, return_tensors=_A      )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       int          =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											UpperCAmelCase_      :       int          =       features['''words''']

								UpperCAmelCase_      :       str          =       self.tokenizer(
								    text=text if text is not None else features['''words''']				, text_pair=text_pair if text_pair is not None else None				, boxes=boxes if boxes is not None else features['''boxes''']				, word_labels=_A				, add_special_tokens=_A				, padding=_A				, truncation=_A				, max_length=_A				, stride=_A				, pad_to_multiple_of=_A				, return_token_type_ids=_A				, return_attention_mask=_A				, return_overflowing_tokens=_A				, return_special_tokens_mask=_A				, return_offsets_mapping=_A				, return_length=_A				, verbose=_A				, return_tensors=_A				, **_A				, )

								# add pixel values
								UpperCAmelCase_      :       int          =       features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
											UpperCAmelCase_      :       List[Any]          =       self.get_overflowing_images(_A				, encoded_inputs['''overflow_to_sample_mapping''']      )
								UpperCAmelCase_      :       Optional[int]          =       images

								return encoded_inputs




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple      )    ->							Dict:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								UpperCAmelCase_      :       Tuple          =       []
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]      )

								if len(_A      ) != len(_A      ):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F" {len(_A      )} and {len(_A      )}"      )

								return images_with_overflow




					def 		A      (  self  :		Optional[Any]				, *_A  :		Union[str, Any]				, **_A  :		Union[str, Any]      )    ->							Tuple:
								return self.tokenizer.batch_decode(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		Optional[Any]				, **_A  :		Tuple      )    ->							Tuple:
								return self.tokenizer.decode(*_A				, **_A      )




					@property
					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								return ["input_ids", "bbox", "attention_mask", "image"]




					@property
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				, _A				, )
								return self.image_processor_class





					@property
					def 		A      (  self  :		Tuple      )    ->							str:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				, _A				, )
								return self.image_processor


